Jim Barksdale, former CEO of Netscape, once said “If we have data, let’s look at data. If all we have are opinions, let’s go with mine.” While Jim may have said this in jest, the exponential boom in data collection indicates that we increasingly prefer to rely on facts rather than conjecture when making business decisions. More data yields greater insights about customer preferences and experiences, internal processes, and security vulnerabilities — just to name a few.

Everything old is new again–that’s a universal law! In our industry, we have talked about Service Level Agreements (SLAs) for a long time. At Sumo, we are proud to continue to be the only Machine Data Analytics Platform as a Service that commits to a query performance SLA.

‍ Log file parsing is the process of analyzing log file data and breaking it down into logical syntactic components. In simple words - you’re extracting meaningful data from logs that can be measured in thousands of lines. There are multiple ways to perform log file parsing: you can write a custom parser or use parsing tools and/or software.

Massively scaling free-text search has always been the holy grail in big data. Many software firms now face the burgeoning challenge of searching through previously untapped data sources and the current trend is far surpassing the petabyte scale. Here at LogDNA we manage free-text search for thousands of customers with distinct traffic profiles across a multitude of Elasticsearch clusters.

Log files in Linux often contain information that can assist in tracking down the cause of issues hampering system or network performance. If you have multiple servers or levels of IT architecture, the number of logs you generate can soon become overwhelming. In this article, we’ll be looking at some ways to ease the burden of managing your Linux logs.

Every log management solution out there has its own alerting feature. Alerts are a critical component of every logging tool. They can tell you whether an event is something you want to check out rather than just normal everyday activity you want to ignore. Graylog’s simplified interface is incredibly accessible to assist you with all the information you need in real-time, yet scalable enough to never compromise the level of detail provided.

Kubernetes is THE buzzword these days. Almost every IT organization is currently using it or is in the process of implementing it as part of their infrastructure. The transition to Kubernetes is complicated, whether a company is using an on-premises, cloud, hybrid, or managed solution, and it usually involves other changes in the codebase, such as shifting to a microservices architecture. While the implementation phase is led by the DevOps team, it requires the participation of the whole R&D group.

Logs provide invaluable information about issues you need to troubleshoot. In some circumstances, that may mean that you have to look back at old logs. For example, you may be running a security audit and need to analyze months-old HTTP request logs for a list of specific IP addresses over a period of time. Or you might need to investigate why a scheduled service never occurred, or run an exhaustive postmortem on incidents that happened over a couple months but that you suspect are related.

CloudTrail logs track actions taken by a user, role, or an AWS service, whether taken through the AWS console or API operations. In contrast to on-premise-infrastructure where something as important as network flow monitoring (Netflow logs) could take weeks or months to get off the ground, AWS has the ability to track flow logs with a few clicks at relatively low cost.

Kubernetes has several key differences that push the limits of traditional application monitoring. Due to the distributed ephemeral nature of Kubernetes, most existing solutions fail to give the visibility we might expect, resulting in longer resolution times. Looking at these potential pitfalls can help guide us as we take a fresh look at Kubernetes management and monitoring.